def SN(self):
"""
`lsst.sims.catsim.SNObject` instance with peakMJD set to t0
"""
if self.snState is not None:
return SNObject.fromSNState(self.snState)
sn = SNObject(ra=self.radeg, dec=self.decdeg)
sn.set(t0=self.t0)
sn.set(z=0.5)
sn.set_source_peakabsmag(self.bessellBpeakabsmag, 'bessellB', 'ab')
return sn
def SN(self):
"""
`lsst.sims.catsim.SNObject` instance with peakMJD set to t0
"""
if self.snState is not None:
return SNObject.fromSNState(self.snState)
sn = SNObject(ra=self.radeg, dec=self.decdeg)
sn.set(t0=self.t0)
sn.set(z=0.5)
sn.set_source_peakabsmag(self.bessellBpeakabsmag, 'bessellB', 'ab')
return sn
def SN(self):
"""
`lsst.sims.catsim.SNObject` instance with peakMJD set to t0
"""
if self._SN is not None:
pass
# return self._SN
elif self.snState is not None:
self._SN = SNObject.fromSNState(self.snState)
else :
sn = SNObject(ra=self.radeg, dec=self.decdeg)
sn.set(t0=self.t0)
sn.set(z=0.5)
sn.set_source_peakabsmag(self.peakAbsMagBesselB, 'bessellB', 'ab')
self._SN = sn
return self._SN
def SN(self):
"""
`lsst.sims.catsim.SNObject` instance with peakMJD set to t0
"""
if self._SN is not None:
pass
# return self._SN
elif self.snState is not None:
self._SN = SNObject.fromSNState(self.snState)
else:
sn = SNObject(ra=self.radeg, dec=self.decdeg)
sn.set(t0=self.t0)
sn.set(z=0.5)
sn.set_source_peakabsmag(self.peakAbsMagBesselB, 'bessellB', 'ab')
self._SN = sn
return self._SN
def _calculate_batch_of_sn(self, sn_truth_params, mjd_arr, filter_arr,
out_dict, tag):
"""
For processing SNe in batches using multiprocessing
-----
sn_truth_params is a numpy array of json-ized
dicts defining the state of an SNObject
mjd_arr is a numpy array of the TAI times of
observations
filter_arr is a numpy array of ints indicating
the filter being observed at each time
out_dict is a multprocessing.Manager().dict() to store
the results
tag is an integer denoting which batch of SNe this is.
"""
int_to_filter = 'ugrizy'
mags = np.NaN*np.ones((len(sn_truth_params), len(mjd_arr)), dtype=float)
for i_obj, sn_par in enumerate(sn_truth_params):
sn_obj = SNObject.fromSNState(json.loads(sn_par))
for i_time in range(len(mjd_arr)):
mjd = mjd_arr[i_time]
filter_name = int_to_filter[filter_arr[i_time]]
if mjd < sn_obj.mintime() or mjd > sn_obj.maxtime():
continue
bp = self._bp_dict[filter_name]
sn_sed = sn_obj.SNObjectSED(mjd, bandpass=bp,
applyExtinction=False)
ff = sn_sed.calcFlux(bp)
if ff>1.0e-300:
mm = sn_sed.magFromFlux(ff)
mags[i_obj][i_time] = mm
out_dict[tag] = mags
class sprinkler():
def __init__(self,
catsim_cat,
visit_mjd,
specFileMap,
sed_path,
om10_cat='twinkles_lenses_v2.fits',
sne_cat='dc2_sne_cat.csv',
density_param=1.,
cached_sprinkling=False,
agn_cache_file=None,
sne_cache_file=None,
defs_file=None):
"""
Parameters
----------
catsim_cat: catsim catalog
The results array from an instance catalog.
visit_mjd: float
The mjd of the visit
specFileMap:
This will tell the instance catalog where to write the files
om10_cat: optional, defaults to 'twinkles_lenses_v2.fits
fits file with OM10 catalog
sne_cat: optional, defaults to 'dc2_sne_cat.csv'
density_param: `np.float`, optioanl, defaults to 1.0
the fraction of eligible agn objects that become lensed and should
be between 0.0 and 1.0.
cached_sprinkling: boolean
If true then pick from a preselected list of galtileids
agn_cache_file: str
sne_cache_file: str
defs_file: str
Returns
-------
updated_catalog:
A new results array with lens systems added.
"""
twinklesDir = getPackageDir('Twinkles')
om10_cat = os.path.join(twinklesDir, 'data', om10_cat)
self.catalog = catsim_cat
# ****** THIS ASSUMES THAT THE ENVIRONMENT VARIABLE OM10_DIR IS SET *******
lensdb = om10.DB(catalog=om10_cat, vb=False)
self.lenscat = lensdb.lenses.copy()
self.density_param = density_param
self.bandpassDict = BandpassDict.loadTotalBandpassesFromFiles(
bandpassNames=['i'])
self.sne_catalog = pd.read_csv(
os.path.join(twinklesDir, 'data', sne_cat))
#self.sne_catalog = self.sne_catalog.iloc[:101] ### Remove this after testing
self.used_systems = []
self.visit_mjd = visit_mjd
self.sn_obj = SNObject(0., 0.)
self.write_dir = specFileMap.subdir_map['(^specFileGLSN)']
self.sed_path = sed_path
self.cached_sprinkling = cached_sprinkling
if self.cached_sprinkling is True:
if ((agn_cache_file is None) | (sne_cache_file is None)):
raise AttributeError(
'Must specify cache files if using cached_sprinkling.')
#agn_cache_file = os.path.join(twinklesDir, 'data', 'test_agn_galtile_cache.csv')
self.agn_cache = pd.read_csv(agn_cache_file)
#sne_cache_file = os.path.join(twinklesDir, 'data', 'test_sne_galtile_cache.csv')
self.sne_cache = pd.read_csv(sne_cache_file)
else:
self.agn_cache = None
self.sne_cache = None
if defs_file is None:
self.defs_file = os.path.join(twinklesDir, 'data',
'catsim_defs.csv')
else:
self.defs_file = defs_file
specFileStart = 'Burst'
for key, val in sorted(iteritems(SpecMap.subdir_map)):
if re.match(key, specFileStart):
galSpecDir = str(val)
self.galDir = str(
getPackageDir('sims_sed_library') + '/' + galSpecDir + '/')
self.imSimBand = Bandpass()
self.imSimBand.imsimBandpass()
#self.LRG_name = 'Burst.25E09.1Z.spec'
#self.LRG = Sed()
#self.LRG.readSED_flambda(str(galDir + self.LRG_name))
#return
#Calculate imsimband magnitudes of source galaxies for matching
agn_fname = str(
getPackageDir('sims_sed_library') + '/agnSED/agn.spec.gz')
src_iband = self.lenscat['MAGI_IN']
src_z = self.lenscat['ZSRC']
self.src_mag_norm = []
for src, s_z in zip(src_iband, src_z):
agn_sed = Sed()
agn_sed.readSED_flambda(agn_fname)
agn_sed.redshiftSED(s_z, dimming=True)
self.src_mag_norm.append(matchBase().calcMagNorm(
[src], agn_sed, self.bandpassDict))
#self.src_mag_norm = matchBase().calcMagNorm(src_iband,
# [agn_sed]*len(src_iband),
#
# self.bandpassDict)
self.defs_dict = {}
with open(self.defs_file, 'r') as f:
for line in f:
line_defs = line.split(',')
if len(line_defs) > 1:
self.defs_dict[line_defs[0]] = line_defs[1].split('\n')[0]
def sprinkle(self):
# Define a list that we can write out to a text file
lenslines = []
# For each galaxy in the catsim catalog
updated_catalog = self.catalog.copy()
# print("Running sprinkler. Catalog Length: ", len(self.catalog))
for rowNum, row in enumerate(self.catalog):
# if rowNum == 100 or rowNum % 100000==0:
# print("Gone through ", rowNum, " lines of catalog.")
if not np.isnan(row[self.defs_dict['galaxyAgn_magNorm']]):
candidates = self.find_lens_candidates(
row[self.defs_dict['galaxyAgn_redshift']],
row[self.defs_dict['galaxyAgn_magNorm']])
#varString = json.loads(row[self.defs_dict['galaxyAgn_varParamStr']])
# varString[self.defs_dict['pars']]['t0_mjd'] = 59300.0
#row[self.defs_dict['galaxyAgn_varParamStr']] = json.dumps(varString)
np.random.seed(row[self.defs_dict['galtileid']] % (2 ^ 32 - 1))
pick_value = np.random.uniform()
# If there aren't any lensed sources at this redshift from OM10 move on the next object
if (((len(candidates) > 0) and
(pick_value <= self.density_param) and
(self.cached_sprinkling is False)) |
((self.cached_sprinkling is True) and
(row[self.defs_dict['galtileid']]
in self.agn_cache['galtileid'].values))):
# Randomly choose one the lens systems
# (can decide with or without replacement)
# Sort first to make sure the same choice is made every time
if self.cached_sprinkling is True:
twinkles_sys_cache = self.agn_cache.query(
'galtileid == %i' %
row[self.defs_dict['galtileid']]
)['twinkles_system'].values[0]
newlens = self.lenscat[np.where(
self.lenscat['twinklesId'] == twinkles_sys_cache)
[0]][0]
else:
candidates = candidates[np.argsort(
candidates['twinklesId'])]
newlens = np.random.choice(candidates)
# Append the lens galaxy
# For each image, append the lens images
for i in range(newlens['NIMG']):
lensrow = row.copy()
# XIMG and YIMG are in arcseconds
# raPhSim and decPhoSim are in radians
#Shift all parts of the lensed object, not just its agn part
for lensPart in [
'galaxyBulge', 'galaxyDisk', 'galaxyAgn'
]:
lens_ra = lensrow[self.defs_dict[str(lensPart +
'_raJ2000')]]
lens_dec = lensrow[self.defs_dict[str(
lensPart + '_decJ2000')]]
delta_ra = np.radians(
newlens['XIMG'][i] / 3600.0) / np.cos(lens_dec)
delta_dec = np.radians(newlens['YIMG'][i] / 3600.0)
lensrow[self.defs_dict[str(
lensPart + '_raJ2000')]] = lens_ra + delta_ra
lensrow[self.defs_dict[
str(lensPart +
'_decJ2000')]] = lens_dec + delta_dec
mag_adjust = 2.5 * np.log10(np.abs(newlens['MAG'][i]))
lensrow[
self.defs_dict['galaxyAgn_magNorm']] -= mag_adjust
varString = json.loads(
lensrow[self.defs_dict['galaxyAgn_varParamStr']])
varString[self.defs_dict['pars']]['t0Delay'] = newlens[
'DELAY'][i]
varString[self.defs_dict[
'varMethodName']] = 'applyAgnTimeDelay'
lensrow[self.defs_dict[
'galaxyAgn_varParamStr']] = json.dumps(varString)
lensrow[self.defs_dict['galaxyDisk_majorAxis']] = 0.0
lensrow[self.defs_dict['galaxyDisk_minorAxis']] = 0.0
lensrow[
self.defs_dict['galaxyDisk_positionAngle']] = 0.0
lensrow[self.defs_dict['galaxyDisk_internalAv']] = 0.0
lensrow[self.defs_dict[
'galaxyDisk_magNorm']] = 999. #np.nan To be fixed post run1.1
lensrow[
self.defs_dict['galaxyDisk_sedFilename']] = None
lensrow[self.defs_dict['galaxyBulge_majorAxis']] = 0.0
lensrow[self.defs_dict['galaxyBulge_minorAxis']] = 0.0
lensrow[
self.defs_dict['galaxyBulge_positionAngle']] = 0.0
lensrow[self.defs_dict['galaxyBulge_internalAv']] = 0.0
lensrow[self.defs_dict[
'galaxyBulge_magNorm']] = 999. #np.nan To be fixed post run1.1
lensrow[
self.defs_dict['galaxyBulge_sedFilename']] = None
lensrow[self.defs_dict[
'galaxyBulge_redshift']] = newlens['ZSRC']
lensrow[self.defs_dict[
'galaxyDisk_redshift']] = newlens['ZSRC']
lensrow[self.defs_dict[
'galaxyAgn_redshift']] = newlens['ZSRC']
#To get back twinklesID in lens catalog from phosim catalog id number
#just use np.right_shift(phosimID-28, 10). Take the floor of the last
#3 numbers to get twinklesID in the twinkles lens catalog and the remainder is
#the image number minus 1.
lensrow[self.defs_dict['galtileid']] = (
lensrow[self.defs_dict['galtileid']] * 10000 +
newlens['twinklesId'] * 4 + i)
updated_catalog = np.append(updated_catalog, lensrow)
#Now manipulate original entry to be the lens galaxy with desired properties
#Start by deleting Disk and AGN properties
if not np.isnan(row[self.defs_dict['galaxyDisk_magNorm']]):
row[self.defs_dict['galaxyDisk_majorAxis']] = 0.0
row[self.defs_dict['galaxyDisk_minorAxis']] = 0.0
row[self.defs_dict['galaxyDisk_positionAngle']] = 0.0
row[self.defs_dict['galaxyDisk_internalAv']] = 0.0
row[self.defs_dict[
'galaxyDisk_magNorm']] = 999. #np.nan To be fixed post run1.1
row[self.defs_dict['galaxyDisk_sedFilename']] = None
row[self.defs_dict[
'galaxyAgn_magNorm']] = None #np.nan To be fixed post run1.1
row[self.defs_dict[
'galaxyDisk_magNorm']] = 999. # To be fixed in run1.1
row[self.defs_dict['galaxyAgn_sedFilename']] = None
#Now insert desired Bulge properties
row[self.defs_dict['galaxyBulge_sedFilename']] = newlens[
'lens_sed']
row[self.
defs_dict['galaxyBulge_redshift']] = newlens['ZLENS']
row[self.
defs_dict['galaxyDisk_redshift']] = newlens['ZLENS']
row[self.
defs_dict['galaxyAgn_redshift']] = newlens['ZLENS']
row_lens_sed = Sed()
row_lens_sed.readSED_flambda(
str(self.galDir + newlens['lens_sed']))
row_lens_sed.redshiftSED(newlens['ZLENS'], dimming=True)
row[self.defs_dict['galaxyBulge_magNorm']] = matchBase(
).calcMagNorm(
[newlens['APMAG_I']], row_lens_sed,
self.bandpassDict) #Changed from i band to imsimband
row[self.defs_dict[
'galaxyBulge_majorAxis']] = radiansFromArcsec(
newlens['REFF'] / np.sqrt(1 - newlens['ELLIP']))
row[self.defs_dict[
'galaxyBulge_minorAxis']] = radiansFromArcsec(
newlens['REFF'] * np.sqrt(1 - newlens['ELLIP']))
#Convert orientation angle to west of north from east of north by *-1.0 and convert to radians
row[self.defs_dict['galaxyBulge_positionAngle']] = newlens[
'PHIE'] * (-1.0) * np.pi / 180.0
#Replace original entry with new entry
updated_catalog[rowNum] = row
else:
if self.cached_sprinkling is True:
if row[self.defs_dict['galtileid']] in self.sne_cache[
'galtileid'].values:
use_system = self.sne_cache.query(
'galtileid == %i' %
row[self.defs_dict['galtileid']]
)['twinkles_system'].values
use_df = self.sne_catalog.query(
'twinkles_sysno == %i' % use_system)
self.used_systems.append(use_system)
else:
continue
else:
lens_sne_candidates = self.find_sne_lens_candidates(
row[self.defs_dict['galaxyDisk_redshift']])
candidate_sysno = np.unique(
lens_sne_candidates['twinkles_sysno'])
num_candidates = len(candidate_sysno)
if num_candidates == 0:
continue
used_already = np.array([
sys_num in self.used_systems
for sys_num in candidate_sysno
])
unused_sysno = candidate_sysno[~used_already]
if len(unused_sysno) == 0:
continue
np.random.seed(row[self.defs_dict['galtileid']] %
(2 ^ 32 - 1))
use_system = np.random.choice(unused_sysno)
use_df = self.sne_catalog.query('twinkles_sysno == %i' %
use_system)
for i in range(len(use_df)):
lensrow = row.copy()
for lensPart in ['galaxyBulge', 'galaxyDisk', 'galaxyAgn']:
lens_ra = lensrow[self.defs_dict[str(lensPart +
'_raJ2000')]]
lens_dec = lensrow[self.defs_dict[str(lensPart +
'_decJ2000')]]
delta_ra = np.radians(
use_df['x'].iloc[i] / 3600.0) / np.cos(lens_dec)
delta_dec = np.radians(use_df['y'].iloc[i] / 3600.0)
lensrow[self.defs_dict[str(
lensPart + '_raJ2000')]] = lens_ra + delta_ra
lensrow[self.defs_dict[str(
lensPart + '_decJ2000')]] = lens_dec + delta_dec
# varString = json.loads(lensrow[self.defs_dict['galaxyAgn_varParamStr']])
varString = 'None'
lensrow[
self.defs_dict['galaxyAgn_varParamStr']] = varString
lensrow[self.defs_dict['galaxyDisk_majorAxis']] = 0.0
lensrow[self.defs_dict['galaxyDisk_minorAxis']] = 0.0
lensrow[self.defs_dict['galaxyDisk_positionAngle']] = 0.0
lensrow[self.defs_dict['galaxyDisk_internalAv']] = 0.0
lensrow[self.defs_dict[
'galaxyDisk_magNorm']] = 999. #np.nan To be fixed post run1.1
lensrow[self.defs_dict['galaxyDisk_sedFilename']] = None
lensrow[self.defs_dict['galaxyBulge_majorAxis']] = 0.0
lensrow[self.defs_dict['galaxyBulge_minorAxis']] = 0.0
lensrow[self.defs_dict['galaxyBulge_positionAngle']] = 0.0
lensrow[self.defs_dict['galaxyBulge_internalAv']] = 0.0
lensrow[self.defs_dict[
'galaxyBulge_magNorm']] = 999. #np.nan To be fixed post run1.1
lensrow[self.defs_dict['galaxyBulge_sedFilename']] = None
z_s = use_df['zs'].iloc[i]
lensrow[self.defs_dict['galaxyBulge_redshift']] = z_s
lensrow[self.defs_dict['galaxyDisk_redshift']] = z_s
lensrow[self.defs_dict['galaxyAgn_redshift']] = z_s
#To get back twinklesID in lens catalog from phosim catalog id number
#just use np.right_shift(phosimID-28, 10). Take the floor of the last
#3 numbers to get twinklesID in the twinkles lens catalog and the remainder is
#the image number minus 1.
lensrow[self.defs_dict['galtileid']] = (
lensrow[self.defs_dict['galtileid']] * 10000 +
use_system * 4 + i)
add_to_cat, sn_magnorm, sn_fname = self.create_sn_sed(
use_df.iloc[i],
lensrow[self.defs_dict['galaxyAgn_raJ2000']],
lensrow[self.defs_dict['galaxyAgn_decJ2000']],
self.visit_mjd)
lensrow[self.defs_dict['galaxyAgn_sedFilename']] = sn_fname
lensrow[self.defs_dict[
'galaxyAgn_magNorm']] = sn_magnorm #This will need to be adjusted to proper band
mag_adjust = 2.5 * np.log10(np.abs(use_df['mu'].iloc[i]))
lensrow[self.defs_dict['galaxyAgn_magNorm']] -= mag_adjust
if add_to_cat is True:
updated_catalog = np.append(updated_catalog, lensrow)
else:
continue
#Now manipulate original entry to be the lens galaxy with desired properties
#Start by deleting Disk and AGN properties
if not np.isnan(row[self.defs_dict['galaxyDisk_magNorm']]):
row[self.defs_dict['galaxyDisk_majorAxis']] = 0.0
row[self.defs_dict['galaxyDisk_minorAxis']] = 0.0
row[self.defs_dict['galaxyDisk_positionAngle']] = 0.0
row[self.defs_dict['galaxyDisk_internalAv']] = 0.0
row[self.defs_dict[
'galaxyDisk_magNorm']] = 999. #np.nan To be fixed post run1.1
row[self.defs_dict['galaxyDisk_sedFilename']] = None
row[self.defs_dict[
'galaxyAgn_magNorm']] = None #np.nan To be fixed post run1.1
row[self.defs_dict[
'galaxyDisk_magNorm']] = 999. #To be fixed post run1.1
row[self.defs_dict['galaxyAgn_sedFilename']] = None
#Now insert desired Bulge properties
row[self.defs_dict['galaxyBulge_sedFilename']] = use_df[
'lens_sed'].iloc[0]
row[self.
defs_dict['galaxyBulge_redshift']] = use_df['zl'].iloc[0]
row[self.
defs_dict['galaxyDisk_redshift']] = use_df['zl'].iloc[0]
row[self.
defs_dict['galaxyAgn_redshift']] = use_df['zl'].iloc[0]
row[self.defs_dict['galaxyBulge_magNorm']] = use_df[
'bulge_magnorm'].iloc[0]
# row[self.defs_dict['galaxyBulge_magNorm']] = matchBase().calcMagNorm([newlens['APMAG_I']], self.LRG, self.bandpassDict) #Changed from i band to imsimband
row[self.
defs_dict['galaxyBulge_majorAxis']] = radiansFromArcsec(
use_df['r_eff'].iloc[0] /
np.sqrt(1 - use_df['e'].iloc[0]))
row[self.
defs_dict['galaxyBulge_minorAxis']] = radiansFromArcsec(
use_df['r_eff'].iloc[0] *
np.sqrt(1 - use_df['e'].iloc[0]))
#Convert orientation angle to west of north from east of north by *-1.0 and convert to radians
row[self.defs_dict['galaxyBulge_positionAngle']] = use_df[
'theta_e'].iloc[0] * (-1.0) * np.pi / 180.0
#Replace original entry with new entry
updated_catalog[rowNum] = row
return updated_catalog
def find_lens_candidates(self, galz, gal_mag):
# search the OM10 catalog for all sources +- 0.1 dex in redshift
# and within .25 mags of the CATSIM source
w = np.where(
(np.abs(np.log10(self.lenscat['ZSRC']) - np.log10(galz)) <= 0.1)
& (np.abs(self.src_mag_norm - gal_mag) <= .25))[0]
lens_candidates = self.lenscat[w]
return lens_candidates
def find_sne_lens_candidates(self, galz):
w = np.where(
(np.abs(np.log10(self.sne_catalog['zs']) - np.log10(galz)) <= 0.1))
lens_candidates = self.sne_catalog.iloc[w]
return lens_candidates
def create_sn_sed(self, system_df, sn_ra, sn_dec, sed_mjd):
sn_param_dict = copy.deepcopy(self.sn_obj.SNstate)
sn_param_dict['_ra'] = sn_ra
sn_param_dict['_dec'] = sn_dec
sn_param_dict['z'] = system_df['zs']
sn_param_dict['c'] = system_df['c']
sn_param_dict['x0'] = system_df['x0']
sn_param_dict['x1'] = system_df['x1']
sn_param_dict['t0'] = system_df['t_start']
# sn_param_dict['t0'] = 61681.083859 #+1500. ### For testing only
current_sn_obj = self.sn_obj.fromSNState(sn_param_dict)
current_sn_obj.mwEBVfromMaps()
wavelen_max = 1800.
wavelen_min = 30.
wavelen_step = 0.1
sn_sed_obj = current_sn_obj.SNObjectSED(time=sed_mjd,
wavelen=np.arange(
wavelen_min, wavelen_max,
wavelen_step))
flux_500 = sn_sed_obj.flambda[np.where(
sn_sed_obj.wavelen >= 499.99)][0]
if flux_500 > 0.:
add_to_cat = True
sn_magnorm = current_sn_obj.catsimBandMag(self.imSimBand, sed_mjd)
sn_name = 'specFileGLSN_%i_%i_%.4f.txt' % (
system_df['twinkles_sysno'], system_df['imno'], sed_mjd)
sed_filename = '%s/%s' % (self.sed_path, sn_name)
sn_sed_obj.writeSED(sed_filename)
with open(sed_filename,
'rb') as f_in, gzip.open(str(sed_filename + '.gz'),
'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(sed_filename)
else:
add_to_cat = False
sn_magnorm = np.nan
sn_name = None
return add_to_cat, sn_magnorm, sn_name
def update_catsim(self):
# Remove the catsim object
# Add lensed images to the catsim given source brightness and magnifications
# Add lens galaxy to catsim
return
def catsim_to_phosim(self):
# Pass this catsim to phosim to make images
return